Analysis of data often involves classifying the data into categories, for example recognising a label that represents an object present in the image. A common method is to determine the relationships between data and labels using an artificial neural network classifier. An artificial neural network is composed of a set of nodes, a set of weights and a set of edges, also referred to as connections. Each of the edges is weighted and connects two nodes of the network. The weighted edges are also referred to as weighted connections. The artificial neural network is trained using a set of training input instances and training targets. The artificial neural network is trained to apply a classification label to new data based on the relationship between training data instances and labels.
A common problem is that artificial neural networks learn classification relationships that produce incorrect labels when used to classify new data. For example an artificial neural network can learn to identify pictures of dogs and correctly apply the label “dog” to the training images. However, when classifying a picture of a dog that is not in the training set, the network incorrectly selects the label “cat” instead of the correct label. The term “overfitting” refers to the situation where an artificial neural network, learns to classify examples the artificial neural network has been trained on with higher accuracy than examples that have not been used in training.
A need exists to address one or more of the problems associated with training an artificial neural network.